A collection of Java applets and simulations covering a range of topics (descriptive statistics, confidence intervals, regression, effect size, ANOVA, etc.).
A collection of Java applets and simulations covering a range of topics (descriptive statistics, confidence intervals, regression, effect size, ANOVA, etc.).
A song for use in helping students identify examples of biased (like the range) and unbiased (like the mean) estimators. Lyrics © 2015 by Larry Lesser, music by Dominic Dousa. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
A song for use in helping students apply margin of error in the context of a poll question, including that variability decreases with the square root of the sample size. Lyrics & Music © 2015 Lawrence M. Lesser. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
A song for use in helping students contrast inferential and descriptive statistics with respect to their different goals and typical tools/outputs. Lyrics and music © 2017 by Greg Crowther.This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
A song for use in helping students to identify counterparts in the courtroom analogy for hypothesis tests (innocence ≈ null; acquit ≈ fail to reject; etc…) and to identify errors of Type I and II in context. Lyrics by Larry Lesser and music by Larry Lesser and Dominic Sousa in 2015, both from The University of Texas at El Paso. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
A song for use in helping students to reason about the factors that affect the width of a confidence interval (sample size, confidence level, and population standard deviation). Lyrics by Larry Lesser and music by Dominic Sousa in 2015, both from The University of Texas at El Paso. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
A song for use in helping students to reason about how larger sample sizes decrease the p-value, all else being equal. Lyrics by Larry Lesser and music by Dominic Sousa in 2015, both from The University of Texas at El Paso. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
A song for use in helping students to identify sample and population quantities in context and match to standard statistical notation. Lyrics by Larry Lesser and music by Dominic Sousa in 2015, both from The University of Texas at El Paso. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
As our economy, society, and daily life become increasingly dependent on data, work across nearly all fields is becoming more data driven, affecting both the jobs that are available and the skills that are required. At the request of the National Science Foundation, the National Academies of Sciences, Engineering, and Medicine were asked to set forth a vision for the emerging discipline of data science at the undergraduate level. The study committee considered the core principles and skills undergraduates should learn and discussed the pedagogical issues that must be addressed to build effective data science education programs. Data Science for Undergraduates: Opportunities and Options underscores the importance of preparing undergraduates for a data-enabled world and recommends that academic institutions and other stakeholders take steps to meet the evolving data science needs of students.
Watch the report release webinar here: https://vimeo.com/269033724
RStudio Cloud makes it easy for professionals, hobbyists, trainers, teachers and students to do, share, teach and learn data science using R. Create analyses using RStudio directly from your browser - there is no software to install and nothing to configure on your computer. Share your projects - and access those of others - without worrying about data transfer or package installation. Each project defines its own environment, and RStudio Cloud automatically reproduces that environment whenever anyone accesses the project. It’s easy to share analyses with the world - but it’s also simple to collaborate with a select group in a private space. You control who can enter a space - and via roles, you have fine grained control over what each user can do. There are also many learning materials available: interactive tutorials covering the basics of data science, cheatsheets for working with popular R packages, links to Datacamp courses, and a guide to using RStudio Cloud.